Antimicrobial drug resistance is a major clinical problem, with resistant strains of bacteria emerging at a rate that dramatically outpaces development of new drugs. Traditionally, studies on antibiotic resistance have focused on genetic changes that confer resistance, such as those encoding mechanisms that block the drug target or modify the drug itself. However, bacteria can also evade antibiotics through expression of transient resistance mechanisms, such as multi-drug efflux pumps that turn on either stochastically or in response to antibiotic stress. Studies have implicated these transient resistance mechanisms in chronic, recalcitrant infections, however, recent research has revealed examples where they also play a critical role in increasing mutation propensity. It is unclear how heterogeneity and temporal variability in expression of resistance genes leads to mutations and what the ultimate implications are for the evolution of drug resistance at the population- level. This proposal addresses this gap directly by measuring expression of resistance genes over time alongside reporters for mutation. These single-cell level studies are joined by population-level experiments that modulate expression of the transient resistance genes while measuring growth under antibiotic stress. A complementary modeling approach uses stochastic models to describe heterogeneity in gene expression, mutation rate, and growth. Our central hypothesis is that heterogeneity in expression of transient resistance genes can lead to single-cell-level differences in mutation rate, both via inducing spontaneous mutations due to elevated endogenous stress in the absence of antibiotics and by extending survival times in the presence of antibiotics. We will test this hypothesis using a quantitative approach that integrates single-cell time-lapse microscopy, stochastic modeling, whole genome sequencing, parallelized continuous culture methods, and optogenetic control. The project is organized around three Aims: (1) Measure expression history of transient resistance genes in cells prior to spontaneous mutation. (2) Quantify time to death of single cells and the evolution of resistance under antibiotic treatment. (3) Control temporal variation of AcrAB efflux pump expression to determine frequency-dependent resistance levels and mutation rate. This research is significant because it links dynamic, single-cell-level effects due to heterogeneity in expression of transient resistance genes to the emergence of population-level increases in resistance. Identifying and eliminating nucleation points for the emergence of drug resistance can inform assessment and treatment approaches.